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Register a new userThe Effects of Spatio-temporal Resolution on Deduced Spicule Properties
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Tiago M. D. Pereira
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Spicules have been observed on the sun for more than a century, typically in chromospheric lines such as H-alpha and Ca II H. Recent work has shown that so-called type II spicules may have a role in providing mass to the corona and the solar wind. In chromospheric filtergrams these spicules are not seen to fall back down, and they are shorter-lived and more dynamic than the spicules that have been classically reported in ground-based observations. Observations of type II spicules with Hinode show fundamentally different properties from what was previously measured. In earlier work we showed that these dynamic type II spicules are the most common type, a view that was not properly identified by early observations.The aim of this work is to investigate the effects of spatio-temporal resolution in the classical spicule measurements. Making use of Hinode data degraded to match the observing conditions of older ground-based studies, we measure the properties of spicules with a semi-automated algorithm. These results are then compared to measurements using the original Hinode data. We find that degrading the data has a significant effect on the measured properties of spicules. Most importantly, the results from the degraded data agree well with older studies (e.g. mean spicule duration more than 5 minutes, and upward apparent velocities of about 25 km/s). These results illustrate how the combination of spicule superposition, low spatial resolution and cadence affect the measured properties of spicules, and that previous measurements can be misleading.
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Measuring small separations between two optical sources, either in space or in time, constitute an important metrological challenge as standard intensity-only measurements fail for vanishing separations. Contrarily, it has been established that appropriate coherent mode projections can appraise arbitrarily small separations with quantum-limited precision. However, the question of whether the optical coherence brings any metrological advantage to mode projections is still a point of debate. Here, we elucidate this problem by experimentally investigating the effect of varying coherence on estimating the temporal separation between two single-photon pulses. We show that, for an accurate interpretation, special attention must be paid to properly normalize the quantum Fisher information to account for the strength of the signal. Our experiment demonstrates that coherent mode projections are optimal for any degree of coherence.
Spatio-temporal data sets are rapidly growing in size. For example, environmental variables are measured with ever-higher resolution by increasing numbers of automated sensors mounted on satellites and aircraft. Using such data, which are typically noisy and incomplete, the goal is to obtain complete maps of the spatio-temporal process, together with proper uncertainty quantification. We focus here on real-time filtering inference in linear Gaussian state-space models. At each time point, the state is a spatial field evaluated on a very large spatial grid, making exact inference using the Kalman filter computationally infeasible. Instead, we propose a multi-resolution filter (MRF), a highly scalable and fully probabilistic filtering method that resolves spatial features at all scales. We prove that the MRF matrices exhibit a particular block-sparse multi-resolution structure that is preserved under filtering operations through time. We also discuss inference on time-varying parameters using an approximate Rao-Blackwellized particle filter, in which the integrated likelihood is computed using the MRF. We compare the MRF to existing approaches in a simulation study and a real satellite-data application.
Solar spicules are the fundamental magnetic structures in the chromosphere and considered to play a key role in channelling the chromosphere and corona. Recently, it was suggested by De Pontieu et al. that there were two types of spicules with very different dynamic properties, which were detected by space- time plot technique in the Ca ii H line (3968 A) wavelength from Hinode/SOT observations. Type I spicule, with a 3-7 minute lifetime, undergoes a cycle of upward and downward motion; in contrast, Type II spicule fades away within dozens of seconds, without descending phase. We are motivated by the fact that for a spicule with complicated 3D motion, the space-time plot, which is made through a slit on a fixed position, could not match the spicule behavior all the time and might lose its real life story. By revisiting the same data sets, we identify and trace 105 and 102 spicules in quiet sun (QS) and coronal hole (CH), respectively, and obtain their statistical dynamic properties. First, we have not found a single convincing example of Type II spicules. Secondly, more than 60% of the identified spicules in each region show a complete cycle, i.e., majority spicules are Type I. Thirdly, the lifetime of spicules in QS and CH are 148 s and 112 s, respectively, but there is no fundamental lifetime difference between the spicules in QS and CH reported earlier. Therefore, the suggestion of coronal heating by Type II spicules should be taken with cautions. Subject headings: Sun: chromosphere Sun:transition region Sun:corona
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance. To simultaneously exploit the spatial and temporal information of videos, employing 3-dimensional (3D) convolutions is a natural approach. However, straight utilizing 3D convolutions may lead to an excessively high computational complexity which restricts the depth of video SR models and thus undermine the performance. In this paper, we present a novel fast spatio-temporal residual network (FSTRN) to adopt 3D convolutions for the video SR task in order to enhance the performance while maintaining a low computational load. Specifically, we propose a fast spatio-temporal residual block (FRB) that divide each 3D filter to the product of two 3D filters, which have considerably lower dimensions. Furthermore, we design a cross-space residual learning that directly links the low-resolution space and the high-resolution space, which can greatly relieve the computational burden on the feature fusion and up-scaling parts. Extensive evaluations and comparisons on benchmark datasets validate the strengths of the proposed approach and demonstrate that the proposed network significantly outperforms the current state-of-the-art methods.
When scale separation in space and time is poor, the alpha effect and turbulent diffusivity have to be replaced by integral kernels. Earlier work in computing these kernels using the test-field method is now generalized to the case in which both spatial and temporal scale separations are poor. The approximate form of the kernel is such that it can be treated in a straightforward manner by solving a partial differential equation for the mean electromotive force. The resulting mean-field equations are solved for oscillatory alpha-shear dynamos as well as alpha^2 dynamos in which alpha is antisymmetric about the equator, making this dynamo also oscillatory. In both cases, the critical values of the dynamo number is lowered by the fact that the dynamo is oscillatory.
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